Overview

Dataset statistics

Number of variables22
Number of observations242559
Missing cells1924222
Missing cells (%)36.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory56.4 MiB
Average record size in memory244.0 B

Variable types

NUM12
UNSUPPORTED7
CAT2
BOOL1

Warnings

operation_car has constant value "242559" Constant
operation_date has a high cardinality: 25383 distinct values High cardinality
index_train has 242559 (100.0%) missing values Missing
danger has 223763 (92.3%) missing values Missing
loaded has 242559 (100.0%) missing values Missing
operation_train has 242559 (100.0%) missing values Missing
rod_train has 242559 (100.0%) missing values Missing
ssp_station_esr has 242559 (100.0%) missing values Missing
ssp_station_id has 242559 (100.0%) missing values Missing
weight_brutto has 242559 (100.0%) missing values Missing
adm is highly skewed (γ1 = 26.46248648) Skewed
operation_st_id is highly skewed (γ1 = 27.18052782) Skewed
df_index has unique values Unique
index_train is an unsupported type, check if it needs cleaning or further analysis Unsupported
loaded is an unsupported type, check if it needs cleaning or further analysis Unsupported
operation_train is an unsupported type, check if it needs cleaning or further analysis Unsupported
rod_train is an unsupported type, check if it needs cleaning or further analysis Unsupported
ssp_station_esr is an unsupported type, check if it needs cleaning or further analysis Unsupported
ssp_station_id is an unsupported type, check if it needs cleaning or further analysis Unsupported
weight_brutto is an unsupported type, check if it needs cleaning or further analysis Unsupported
receiver has 55854 (23.0%) zeros Zeros
sender has 33571 (13.8%) zeros Zeros

Reproduction

Analysis started2021-04-14 19:46:51.261742
Analysis finished2021-04-14 19:47:48.078970
Duration56.82 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct242559
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1941370.804
Minimum5
Maximum4189902
Zeros0
Zeros (%)0.0%
Memory size1.9 MiB
2021-04-14T22:47:48.306386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile199585.5
Q1952495
median1850939
Q32793773.5
95-th percentile4029769.5
Maximum4189902
Range4189897
Interquartile range (IQR)1841278.5

Descriptive statistics

Standard deviation1166537.77
Coefficient of variation (CV)0.6008835443
Kurtosis-0.9912977725
Mean1941370.804
Median Absolute Deviation (MAD)919207
Skewness0.2148600901
Sum4.708969609e+11
Variance1.360810368e+12
MonotocityStrictly increasing
2021-04-14T22:47:48.484955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
34979961< 0.1%
 
38719451< 0.1%
 
26758891< 0.1%
 
10989311< 0.1%
 
37306141< 0.1%
 
1664441< 0.1%
 
5623611< 0.1%
 
26554191< 0.1%
 
26677091< 0.1%
 
21413741< 0.1%
 
21393271< 0.1%
 
40936431< 0.1%
 
2285481< 0.1%
 
17993651< 0.1%
 
17727461< 0.1%
 
36814541< 0.1%
 
33435631< 0.1%
 
4387001< 0.1%
 
38760471< 0.1%
 
5548421< 0.1%
 
9407761< 0.1%
 
23584841< 0.1%
 
1528011< 0.1%
 
38330601< 0.1%
 
1589501< 0.1%
 
Other values (242534)242534> 99.9%
 
ValueCountFrequency (%) 
51< 0.1%
 
191< 0.1%
 
221< 0.1%
 
271< 0.1%
 
351< 0.1%
 
391< 0.1%
 
501< 0.1%
 
511< 0.1%
 
521< 0.1%
 
691< 0.1%
 
ValueCountFrequency (%) 
41899021< 0.1%
 
41898871< 0.1%
 
41898841< 0.1%
 
41898731< 0.1%
 
41898711< 0.1%
 
41898511< 0.1%
 
41898491< 0.1%
 
41898471< 0.1%
 
41898451< 0.1%
 
41898271< 0.1%
 

index_train
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing242559
Missing (%)100.0%
Memory size1.9 MiB

length
Real number (ℝ≥0)

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.076795996
Minimum0.79
Maximum2.13
Zeros0
Zeros (%)0.0%
Memory size1.9 MiB
2021-04-14T22:47:48.668274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.79
5-th percentile0.87
Q11
median1
Q31
95-th percentile1.82
Maximum2.13
Range1.34
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.244850207
Coefficient of variation (CV)0.227387739
Kurtosis4.82369406
Mean1.076795996
Median Absolute Deviation (MAD)0
Skewness2.478727108
Sum261186.56
Variance0.05995162384
MonotocityNot monotonic
2021-04-14T22:47:48.856184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%) 
117625472.7%
 
0.87192187.9%
 
1.06138185.7%
 
1.82106364.4%
 
1.8563412.6%
 
1.4161662.5%
 
1.8327581.1%
 
1.2210450.4%
 
0.838330.3%
 
1.368290.3%
 
0.865430.2%
 
1.114160.2%
 
1.013810.2%
 
0.793600.1%
 
1.63430.1%
 
1.893360.1%
 
0.853290.1%
 
1.272620.1%
 
1.322230.1%
 
1.231930.1%
 
1.421900.1%
 
1.051650.1%
 
1.21630.1%
 
1.351580.1%
 
1.031520.1%
 
Other values (23)4470.2%
 
ValueCountFrequency (%) 
0.793600.1%
 
0.838330.3%
 
0.853290.1%
 
0.865430.2%
 
0.87192187.9%
 
0.881< 0.1%
 
0.91< 0.1%
 
0.9215< 0.1%
 
0.9521< 0.1%
 
0.991< 0.1%
 
ValueCountFrequency (%) 
2.1346< 0.1%
 
1.9230< 0.1%
 
1.893360.1%
 
1.8563412.6%
 
1.8458< 0.1%
 
1.8327581.1%
 
1.82106364.4%
 
1.811< 0.1%
 
1.782< 0.1%
 
1.7717< 0.1%
 

car_number
Real number (ℝ≥0)

Distinct201622
Distinct (%)83.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62898482.06
Minimum24107542
Maximum98099997
Zeros0
Zeros (%)0.0%
Memory size1.9 MiB
2021-04-14T22:47:49.165333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum24107542
5-th percentile51594265
Q154968421
median60914074
Q363727609.5
95-th percentile94654894
Maximum98099997
Range73992455
Interquartile range (IQR)8759188.5

Descriptive statistics

Standard deviation12807543.8
Coefficient of variation (CV)0.2036224625
Kurtosis2.051961618
Mean62898482.06
Median Absolute Deviation (MAD)4821314
Skewness1.45274081
Sum1.525659291e+13
Variance1.640331783e+14
MonotocityNot monotonic
2021-04-14T22:47:49.367690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5248040714< 0.1%
 
6423538513< 0.1%
 
6493993713< 0.1%
 
6347957013< 0.1%
 
6490321413< 0.1%
 
5248096913< 0.1%
 
6351461613< 0.1%
 
6243805612< 0.1%
 
5248002712< 0.1%
 
5411017612< 0.1%
 
5248023312< 0.1%
 
6214364912< 0.1%
 
6331063512< 0.1%
 
6301014412< 0.1%
 
5248032412< 0.1%
 
5508439611< 0.1%
 
6364285411< 0.1%
 
6347600611< 0.1%
 
6337320311< 0.1%
 
6244990511< 0.1%
 
6342377611< 0.1%
 
6390188811< 0.1%
 
5618486411< 0.1%
 
5411002811< 0.1%
 
5508456011< 0.1%
 
Other values (201597)24226199.9%
 
ValueCountFrequency (%) 
241075421< 0.1%
 
242735591< 0.1%
 
242938211< 0.1%
 
243218381< 0.1%
 
243456701< 0.1%
 
244674411< 0.1%
 
245763731< 0.1%
 
245765061< 0.1%
 
245905231< 0.1%
 
245934851< 0.1%
 
ValueCountFrequency (%) 
980999971< 0.1%
 
980999891< 0.1%
 
980999711< 0.1%
 
980999631< 0.1%
 
980999551< 0.1%
 
980999481< 0.1%
 
980999301< 0.1%
 
980999221< 0.1%
 
980999141< 0.1%
 
980999061< 0.1%
 

destination_esr
Real number (ℝ≥0)

Distinct1190
Distinct (%)0.5%
Missing2366
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean699123.7066
Minimum10002
Maximum997502
Zeros0
Zeros (%)0.0%
Memory size1.9 MiB
2021-04-14T22:47:49.581032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10002
5-th percentile35902
Q1467004
median864601
Q3967808
95-th percentile986103
Maximum997502
Range987500
Interquartile range (IQR)500804

Descriptive statistics

Standard deviation347089.5501
Coefficient of variation (CV)0.4964637114
Kurtosis-0.7118203195
Mean699123.7066
Median Absolute Deviation (MAD)117501
Skewness-0.9848874575
Sum1.679246205e+11
Variance1.204711558e+11
MonotocityNot monotonic
2021-04-14T22:47:49.786267image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
986103216258.9%
 
76404177797.3%
 
967808169427.0%
 
86420777843.2%
 
1850257202.4%
 
94700556012.3%
 
98470047662.0%
 
86460142801.8%
 
86020639921.6%
 
59220438531.6%
 
98780133011.4%
 
98570232831.4%
 
98020029671.2%
 
93730429261.2%
 
2070625781.1%
 
98930924991.0%
 
81760022700.9%
 
6000122560.9%
 
98550522080.9%
 
19100021230.9%
 
18350220580.8%
 
46700420050.8%
 
93220719980.8%
 
92460519650.8%
 
87190618800.8%
 
Other values (1165)11153446.0%
 
(Missing)23661.0%
 
ValueCountFrequency (%) 
100022< 0.1%
 
1030316< 0.1%
 
118043< 0.1%
 
130001< 0.1%
 
149061700.1%
 
154005< 0.1%
 
157014610.2%
 
160094050.2%
 
1640322< 0.1%
 
1700137< 0.1%
 
ValueCountFrequency (%) 
99750211< 0.1%
 
9948051< 0.1%
 
99330411< 0.1%
 
99120524< 0.1%
 
99110162< 0.1%
 
99070052< 0.1%
 
9906071< 0.1%
 
9900053< 0.1%
 
98930924991.0%
 
9892051< 0.1%
 

adm
Real number (ℝ≥0)

SKEWED

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.04017167
Minimum20
Maximum99
Zeros0
Zeros (%)0.0%
Memory size1.9 MiB
2021-04-14T22:47:49.974083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median20
Q320
95-th percentile20
Maximum99
Range79
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5198687065
Coefficient of variation (CV)0.02594133
Kurtosis2442.579832
Mean20.04017167
Median Absolute Deviation (MAD)0
Skewness26.46248648
Sum4860924
Variance0.270263472
MonotocityNot monotonic
2021-04-14T22:47:50.117105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
2024084799.3%
 
2613180.5%
 
272030.1%
 
211570.1%
 
2516< 0.1%
 
2414< 0.1%
 
222< 0.1%
 
991< 0.1%
 
591< 0.1%
 
ValueCountFrequency (%) 
2024084799.3%
 
211570.1%
 
222< 0.1%
 
2414< 0.1%
 
2516< 0.1%
 
2613180.5%
 
272030.1%
 
591< 0.1%
 
991< 0.1%
 
ValueCountFrequency (%) 
991< 0.1%
 
591< 0.1%
 
272030.1%
 
2613180.5%
 
2516< 0.1%
 
2414< 0.1%
 
222< 0.1%
 
211570.1%
 
2024084799.3%
 

danger
Boolean

MISSING

Distinct1
Distinct (%)< 0.1%
Missing223763
Missing (%)92.3%
Memory size1.9 MiB
1
 
18796
(Missing)
223763 
ValueCountFrequency (%) 
1187967.7%
 
(Missing)22376392.3%
 
2021-04-14T22:47:50.234068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

gruz
Real number (ℝ≥0)

Distinct348
Distinct (%)0.1%
Missing60
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean163058.4186
Minimum3009
Maximum757325
Zeros0
Zeros (%)0.0%
Memory size1.9 MiB
2021-04-14T22:47:50.344611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3009
5-th percentile3009
Q1161043
median161128
Q3161170
95-th percentile331016
Maximum757325
Range754316
Interquartile range (IQR)127

Descriptive statistics

Standard deviation91787.19135
Coefficient of variation (CV)0.5629098584
Kurtosis8.637154073
Mean163058.4186
Median Absolute Deviation (MAD)57
Skewness1.649320022
Sum3.954150346e+10
Variance8424888496
MonotocityNot monotonic
2021-04-14T22:47:50.531518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1611286969128.7%
 
30092892511.9%
 
161043189837.8%
 
161185148356.1%
 
161113137625.7%
 
161170108264.5%
 
161096105124.3%
 
39149863882.6%
 
14109261852.5%
 
16114761022.5%
 
21403956322.3%
 
16101648402.0%
 
16113239981.6%
 
17103039681.6%
 
21105632231.3%
 
22106627091.1%
 
9111821250.9%
 
28104815640.6%
 
16115114500.6%
 
23603814010.6%
 
8118813350.6%
 
33101612480.5%
 
15106012360.5%
 
23243111590.5%
 
16106211030.5%
 
Other values (323)192998.0%
 
ValueCountFrequency (%) 
30092892511.9%
 
110052230.1%
 
130002< 0.1%
 
1400317< 0.1%
 
1500613< 0.1%
 
1802374< 0.1%
 
1810813< 0.1%
 
181121< 0.1%
 
210791< 0.1%
 
411233< 0.1%
 
ValueCountFrequency (%) 
7573252< 0.1%
 
7541151< 0.1%
 
7310623< 0.1%
 
7260791< 0.1%
 
7255021500.1%
 
7214843210.1%
 
71145933< 0.1%
 
71144419< 0.1%
 
7112857< 0.1%
 
71103526< 0.1%
 

loaded
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing242559
Missing (%)100.0%
Memory size1.9 MiB

operation_car
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
10
242559 
ValueCountFrequency (%) 
10242559100.0%
 
2021-04-14T22:47:50.731895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-14T22:47:50.833337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:50.930744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
048511850.0%
 
124255925.0%
 
.24255925.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number72767775.0%
 
Other Punctuation24255925.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
048511866.7%
 
124255933.3%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.242559100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common970236100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
048511850.0%
 
124255925.0%
 
.24255925.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII970236100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
048511850.0%
 
124255925.0%
 
.24255925.0%
 

operation_date
Categorical

HIGH CARDINALITY

Distinct25383
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2020-07-30 16:42:00
 
220
2020-07-18 16:52:00
 
195
2020-07-25 16:11:00
 
171
2020-07-29 16:54:00
 
171
2020-07-20 09:06:00
 
168
Other values (25378)
241634 
ValueCountFrequency (%) 
2020-07-30 16:42:002200.1%
 
2020-07-18 16:52:001950.1%
 
2020-07-25 16:11:001710.1%
 
2020-07-29 16:54:001710.1%
 
2020-07-20 09:06:001680.1%
 
2020-07-16 16:43:001650.1%
 
2020-07-23 16:38:001630.1%
 
2020-07-25 17:07:001590.1%
 
2020-07-31 18:24:001590.1%
 
2020-07-17 17:30:001570.1%
 
2020-07-20 16:10:001570.1%
 
2020-07-22 08:39:001530.1%
 
2020-07-17 16:58:001530.1%
 
2020-07-16 15:08:001530.1%
 
2020-07-30 16:19:001500.1%
 
2020-07-29 16:56:001500.1%
 
2020-07-29 16:59:001490.1%
 
2020-07-28 16:51:001470.1%
 
2020-07-17 15:10:001450.1%
 
2020-07-30 16:47:001450.1%
 
2020-07-18 10:20:001440.1%
 
2020-07-19 17:07:001430.1%
 
2020-07-23 13:37:001430.1%
 
2020-07-22 08:32:001410.1%
 
2020-07-23 16:19:001390.1%
 
Other values (25358)23861998.4%
 
2021-04-14T22:47:51.179626image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique7954 ?
Unique (%)3.3%
2021-04-14T22:47:51.558948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length19
Mean length19
Min length19

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories4 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0141745430.8%
 
272334415.7%
 
-48511810.5%
 
:48511810.5%
 
13427737.4%
 
73257077.1%
 
2425595.3%
 
31274672.8%
 
51161372.5%
 
4977032.1%
 
6951182.1%
 
9751531.6%
 
8749701.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number339582673.7%
 
Dash Punctuation48511810.5%
 
Other Punctuation48511810.5%
 
Space Separator2425595.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0141745441.7%
 
272334421.3%
 
134277310.1%
 
73257079.6%
 
31274673.8%
 
51161373.4%
 
4977032.9%
 
6951182.8%
 
9751532.2%
 
8749702.2%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-485118100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
242559100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
:485118100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common4608621100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0141745430.8%
 
272334415.7%
 
-48511810.5%
 
:48511810.5%
 
13427737.4%
 
73257077.1%
 
2425595.3%
 
31274672.8%
 
51161372.5%
 
4977032.1%
 
6951182.1%
 
9751531.6%
 
8749701.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4608621100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0141745430.8%
 
272334415.7%
 
-48511810.5%
 
:48511810.5%
 
13427737.4%
 
73257077.1%
 
2425595.3%
 
31274672.8%
 
51161372.5%
 
4977032.1%
 
6951182.1%
 
9751531.6%
 
8749701.6%
 

operation_st_esr
Real number (ℝ≥0)

Distinct329
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean890745.2695
Minimum831203
Maximum998100
Zeros0
Zeros (%)0.0%
Memory size1.9 MiB
2021-04-14T22:47:51.714154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum831203
5-th percentile852708
Q1863007
median871906
Q3924501
95-th percentile980200
Maximum998100
Range166897
Interquartile range (IQR)61494

Descriptive statistics

Standard deviation38944.25544
Coefficient of variation (CV)0.04372097925
Kurtosis-0.04579097477
Mean890745.2695
Median Absolute Deviation (MAD)11903
Skewness1.044461844
Sum2.160582818e+11
Variance1516655032
MonotocityNot monotonic
2021-04-14T22:47:51.897712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
863007200318.3%
 
937906108874.5%
 
88760396034.0%
 
88790488503.6%
 
86220179993.3%
 
86490279043.3%
 
86210878823.2%
 
93220761782.5%
 
86470552182.2%
 
86480948952.0%
 
94700546911.9%
 
98020042151.7%
 
98610341371.7%
 
87180239051.6%
 
86260635581.5%
 
87270134951.4%
 
85270834141.4%
 
87130433141.4%
 
86230530931.3%
 
83150430931.3%
 
89210329731.2%
 
86310029691.2%
 
97040628581.2%
 
86370228311.2%
 
88900727571.1%
 
Other values (304)10180942.0%
 
ValueCountFrequency (%) 
8312033740.2%
 
83140040< 0.1%
 
83150430931.3%
 
8316083< 0.1%
 
8336034< 0.1%
 
8341081< 0.1%
 
8361033< 0.1%
 
8373031< 0.1%
 
8376048< 0.1%
 
83770864< 0.1%
 
ValueCountFrequency (%) 
9981005< 0.1%
 
9975021< 0.1%
 
99540356< 0.1%
 
9948056< 0.1%
 
9933049< 0.1%
 
9911014< 0.1%
 
9893091990.1%
 
989205118< 0.1%
 
9890088< 0.1%
 
9888047< 0.1%
 

operation_st_id
Real number (ℝ≥0)

SKEWED

Distinct329
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2268180094
Minimum2000035090
Maximum2.000800023e+11
Zeros0
Zeros (%)0.0%
Memory size1.9 MiB
2021-04-14T22:47:52.109019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2000035090
5-th percentile2000035530
Q12000037064
median2001930776
Q32001931256
95-th percentile2002025275
Maximum2.000800023e+11
Range1.980799672e+11
Interquartile range (IQR)1894192

Descriptive statistics

Standard deviation7267938794
Coefficient of variation (CV)3.204304109
Kurtosis736.7871799
Mean2268180094
Median Absolute Deviation (MAD)2746
Skewness27.18052782
Sum5.501674954e+14
Variance5.282293432e+19
MonotocityNot monotonic
2021-04-14T22:47:52.296968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2001933494200318.3%
 
2000037532108874.5%
 
200003553096034.0%
 
200003556488503.6%
 
200193077679993.3%
 
200003990879043.3%
 
200193079478823.2%
 
200003706461782.5%
 
200193350252182.2%
 
200193082248952.0%
 
200202527546911.9%
 
200202560742151.7%
 
200202566141371.7%
 
200193352239051.6%
 
200193078235581.5%
 
200193125634951.4%
 
200193073434141.4%
 
200193087233141.4%
 
200193077830931.3%
 
200193053430931.3%
 
200003589029731.2%
 
200193079829691.2%
 
200003863428581.2%
 
200193081228311.2%
 
200003569227571.1%
 
Other values (304)10180942.0%
 
ValueCountFrequency (%) 
200003509011< 0.1%
 
200003511012< 0.1%
 
20000351301< 0.1%
 
200003514069< 0.1%
 
200003516220< 0.1%
 
200003519415250.6%
 
20000352161< 0.1%
 
20000352301260.1%
 
20000352326160.3%
 
200003525211720.5%
 
ValueCountFrequency (%) 
2.000800023e+113270.1%
 
200203487917< 0.1%
 
20020266092< 0.1%
 
200202566141371.7%
 
200202561377< 0.1%
 
20020256116270.3%
 
20020256096750.3%
 
200202560742151.7%
 
200202527546911.9%
 
200202482988< 0.1%
 

operation_train
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing242559
Missing (%)100.0%
Memory size1.9 MiB

receiver
Real number (ℝ≥0)

ZEROS

Distinct1819
Distinct (%)0.8%
Missing60
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean21249378.92
Minimum0
Maximum99294283
Zeros55854
Zeros (%)23.0%
Memory size1.9 MiB
2021-04-14T22:47:52.502533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1105199
median1126648
Q339513543
95-th percentile79601286
Maximum99294283
Range99294283
Interquartile range (IQR)39408344

Descriptive statistics

Standard deviation29510096.52
Coefficient of variation (CV)1.388751014
Kurtosis-0.05956922076
Mean21249378.92
Median Absolute Deviation (MAD)1126648
Skewness1.164314481
Sum5.152953139e+12
Variance8.708457964e+14
MonotocityNot monotonic
2021-04-14T22:47:52.695311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
05585423.0%
 
1126631163396.7%
 
71479207154446.4%
 
39513543118794.9%
 
575767693713.9%
 
112539957202.4%
 
112602254792.3%
 
2663568742811.8%
 
575766538991.6%
 
112664833011.4%
 
1639723530041.2%
 
7063996225781.1%
 
10563825651.1%
 
4349093124861.0%
 
2664409623511.0%
 
7960128623321.0%
 
112601623020.9%
 
7311603522910.9%
 
18642422360.9%
 
9772819721480.9%
 
10519919440.8%
 
3115796519170.8%
 
16021218630.8%
 
18668318550.8%
 
9809804818100.7%
 
Other values (1794)7725031.8%
 
ValueCountFrequency (%) 
05585423.0%
 
596259< 0.1%
 
832624550.2%
 
10309416520.7%
 
10518210300.4%
 
10519919440.8%
 
1052074550.2%
 
10521311940.5%
 
10523611820.5%
 
1054571640.1%
 
ValueCountFrequency (%) 
992942835< 0.1%
 
991493162< 0.1%
 
9893368625< 0.1%
 
987705114< 0.1%
 
987681582< 0.1%
 
982887834< 0.1%
 
981003311< 0.1%
 
9809804818100.7%
 
980610512< 0.1%
 
978947606< 0.1%
 

rodvag
Real number (ℝ≥0)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.14466171
Minimum20
Maximum99
Zeros0
Zeros (%)0.0%
Memory size1.9 MiB
2021-04-14T22:47:52.862144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile60
Q160
median60
Q360
95-th percentile96
Maximum99
Range79
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.49677929
Coefficient of variation (CV)0.2191677895
Kurtosis1.309235417
Mean66.14466171
Median Absolute Deviation (MAD)0
Skewness0.8915704381
Sum16043983
Variance210.1566098
MonotocityNot monotonic
2021-04-14T22:47:53.003989image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
6017627772.7%
 
963557014.7%
 
70185107.6%
 
9041411.7%
 
2030601.3%
 
4026621.1%
 
9310120.4%
 
955420.2%
 
925320.2%
 
872520.1%
 
991< 0.1%
 
ValueCountFrequency (%) 
2030601.3%
 
4026621.1%
 
6017627772.7%
 
70185107.6%
 
872520.1%
 
9041411.7%
 
925320.2%
 
9310120.4%
 
955420.2%
 
963557014.7%
 
ValueCountFrequency (%) 
991< 0.1%
 
963557014.7%
 
955420.2%
 
9310120.4%
 
925320.2%
 
9041411.7%
 
872520.1%
 
70185107.6%
 
6017627772.7%
 
4026621.1%
 

rod_train
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing242559
Missing (%)100.0%
Memory size1.9 MiB

sender
Real number (ℝ≥0)

ZEROS

Distinct878
Distinct (%)0.4%
Missing60
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean38753544.79
Minimum0
Maximum99863723
Zeros33571
Zeros (%)13.8%
Memory size1.9 MiB
2021-04-14T22:47:53.191633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110891709
median26649063
Q368861216
95-th percentile93149858
Maximum99863723
Range99863723
Interquartile range (IQR)57969507

Descriptive statistics

Standard deviation32066395.53
Coefficient of variation (CV)0.8274441914
Kurtosis-1.303849342
Mean38753544.79
Median Absolute Deviation (MAD)26488851
Skewness0.3365886482
Sum9.397695859e+12
Variance1.028253723e+15
MonotocityNot monotonic
2021-04-14T22:47:53.384281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
03357113.8%
 
14788090159746.6%
 
80298858145386.0%
 
26635687129685.3%
 
12776421107884.4%
 
9314985891053.8%
 
4813418790973.8%
 
5547282664172.6%
 
8119510364042.6%
 
2664409663532.6%
 
7846542160902.5%
 
3221440648572.0%
 
575767648462.0%
 
1089170946191.9%
 
2664890337781.6%
 
5499889734851.4%
 
5306720534671.4%
 
2664888531791.3%
 
5556506030321.3%
 
6768073128981.2%
 
5814690328531.2%
 
9442138627321.1%
 
18811027231.1%
 
5304955526281.1%
 
9877051125991.1%
 
Other values (853)6349826.2%
 
ValueCountFrequency (%) 
03357113.8%
 
444741460.1%
 
1052071< 0.1%
 
1054579< 0.1%
 
1087081< 0.1%
 
1097832< 0.1%
 
1599537< 0.1%
 
16020675< 0.1%
 
16021212280.5%
 
1612462050.1%
 
ValueCountFrequency (%) 
998637238< 0.1%
 
994154918310.3%
 
9902996019< 0.1%
 
9877051125991.1%
 
981134111< 0.1%
 
9811212715< 0.1%
 
981087001< 0.1%
 
981034011< 0.1%
 
980980315< 0.1%
 
979909727< 0.1%
 

ssp_station_esr
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing242559
Missing (%)100.0%
Memory size1.9 MiB

ssp_station_id
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing242559
Missing (%)100.0%
Memory size1.9 MiB

tare_weight
Real number (ℝ≥0)

Distinct217
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean240.8990184
Minimum2
Maximum588
Zeros0
Zeros (%)0.0%
Memory size1.9 MiB
2021-04-14T22:47:53.584870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile220
Q1235
median240
Q3246
95-th percentile265
Maximum588
Range586
Interquartile range (IQR)11

Descriptive statistics

Standard deviation14.85898982
Coefficient of variation (CV)0.06168140459
Kurtosis31.37937321
Mean240.8990184
Median Absolute Deviation (MAD)5
Skewness2.342966643
Sum58432225
Variance220.7895784
MonotocityNot monotonic
2021-04-14T22:47:53.762551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2403310413.6%
 
2452499810.3%
 
250156446.4%
 
235121385.0%
 
24798994.1%
 
24392563.8%
 
23888893.7%
 
23683963.5%
 
24881243.3%
 
23375143.1%
 
23771482.9%
 
24264852.7%
 
24163632.6%
 
22560862.5%
 
23956712.3%
 
23453542.2%
 
24447001.9%
 
22446791.9%
 
23042011.7%
 
24637221.5%
 
23229511.2%
 
24929081.2%
 
23121630.9%
 
22921470.9%
 
26718300.8%
 
Other values (192)3818915.7%
 
ValueCountFrequency (%) 
21< 0.1%
 
1723< 0.1%
 
1732< 0.1%
 
18025< 0.1%
 
1818< 0.1%
 
1831< 0.1%
 
1841< 0.1%
 
1856< 0.1%
 
1861< 0.1%
 
1877< 0.1%
 
ValueCountFrequency (%) 
5882< 0.1%
 
5872< 0.1%
 
5862< 0.1%
 
5651< 0.1%
 
5241< 0.1%
 
5041< 0.1%
 
4752< 0.1%
 
4592< 0.1%
 
4584< 0.1%
 
4572< 0.1%
 

weight_brutto
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing242559
Missing (%)100.0%
Memory size1.9 MiB

Interactions

2021-04-14T22:47:03.744543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:04.009798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:04.262923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:04.529178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:04.773199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:05.034482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:05.297575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:05.549320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:05.814589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:06.064666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:06.316444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:06.560570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:06.807695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:07.061885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:07.307601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:07.586941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:07.835991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:08.105705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:08.374996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:08.639880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:08.915183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:09.168874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:09.428574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:09.684483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:09.932874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:10.188082image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:10.446918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:10.734776image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:10.993983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:11.267345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:11.544570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:11.831613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:12.144939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:12.417924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:12.700319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:12.962864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:13.232501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:13.476817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:13.731476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:14.008198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:14.253655image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:14.558205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:14.829948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:15.715858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:16.014564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:16.269992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:16.536849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:16.829580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:17.102032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:17.362833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:17.628785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:17.915960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:18.171718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:18.444680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:18.715107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:18.988205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:19.264596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:19.515450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:19.789754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:20.057196image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:20.326040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:20.575788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:20.837612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:21.164505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:21.475660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:21.789992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:22.132838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:22.475377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:22.831895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:23.184634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:23.546947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:23.909427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:24.250099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:24.588408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:24.940792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:25.290004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:25.610910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:25.968238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:26.346399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:26.681220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:27.200102image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:27.504599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:27.829642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:28.132362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:28.412872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:28.678882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:28.956906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:29.251117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:29.505693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:29.786537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:30.078891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:30.354296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:30.637742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:30.905593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:31.187746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:31.477496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:31.775844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:32.047984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:32.306920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:32.572506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:32.808707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:33.073638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:33.336582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:33.598759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:33.864615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:34.114146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:34.377928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:34.658668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:34.926593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:35.225007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:35.545840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:35.852539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:36.127156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:36.397952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:36.671860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:36.959849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:37.252653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:37.512623image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:37.786598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:38.059051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:38.326982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:38.573627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:38.820954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:39.094517image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:39.331374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:39.742833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:40.002030image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:40.259533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:40.526947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:40.770408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:41.039526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:41.319415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:41.582642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:41.831688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:42.099253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:42.370034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:42.611730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:42.870957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:43.133636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:43.395036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:43.662575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:43.911717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:44.181894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:44.441213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-04-14T22:47:53.968054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-14T22:47:54.417005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-14T22:47:54.938772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-14T22:47:55.494750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-04-14T22:47:45.109365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:46.154300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:47.242372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:47:47.601275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Sample

First rows

df_indexindex_trainlengthcar_numberdestination_esradmdangergruzloadedoperation_caroperation_dateoperation_st_esroperation_st_idoperation_trainreceiverrodvagrod_trainsenderssp_station_esrssp_station_idtare_weightweight_brutto
05NaN1.062826326460005.020.0NaN161170.0NaN10.02020-07-16 06:30:00862409.02.001933e+09NaN0.060.0NaN14788090.0NaNNaN241.0NaN
119NaN1.06284507818502.020.0NaN161128.0NaN10.02020-07-16 13:39:00872701.02.001931e+09NaN1125399.060.0NaN80298858.0NaNNaN245.0NaN
222NaN1.062844568986103.020.0NaN161147.0NaN10.02020-07-16 14:08:00862803.02.001931e+09NaN1126631.060.0NaN14788090.0NaNNaN245.0NaN
327NaN1.062846316592204.020.0NaN161043.0NaN10.02020-07-16 15:12:00863702.02.001931e+09NaN5757665.060.0NaN32214406.0NaNNaN249.0NaN
435NaN1.062843222986103.020.0NaN161128.0NaN10.02020-07-16 15:04:00863007.02.001933e+09NaN1126631.060.0NaN14788090.0NaNNaN245.0NaN
539NaN1.062843115986103.020.0NaN161185.0NaN10.02020-07-16 16:19:00865604.02.001931e+09NaN1126631.060.0NaN14788090.0NaNNaN245.0NaN
650NaN1.062842943986103.020.0NaN161128.0NaN10.02020-07-16 15:04:00863007.02.001933e+09NaN1126631.060.0NaN14788090.0NaNNaN245.0NaN
751NaN1.06284431176404.020.0NaN161113.0NaN10.02020-07-16 10:55:00863007.02.001933e+09NaN39513543.060.0NaN26635687.0NaNNaN245.0NaN
852NaN1.062842778986103.020.0NaN161128.0NaN10.02020-07-16 15:04:00863007.02.001933e+09NaN1126631.060.0NaN14788090.0NaNNaN245.0NaN
969NaN1.06284334718502.020.0NaN161128.0NaN10.02020-07-16 14:22:00872701.02.001931e+09NaN1125399.060.0NaN80298858.0NaNNaN245.0NaN

Last rows

df_indexindex_trainlengthcar_numberdestination_esradmdangergruzloadedoperation_caroperation_dateoperation_st_esroperation_st_idoperation_trainreceiverrodvagrod_trainsenderssp_station_esrssp_station_idtare_weightweight_brutto
2425494189827NaN1.062817580970406.020.0NaN161170.0NaN10.02020-07-16 15:19:00871802.02.001934e+09NaN98098048.060.0NaN93149858.0NaNNaN249.0NaN
2425504189845NaN1.062818489967808.020.0NaN161128.0NaN10.02020-07-16 16:04:00862108.02.001931e+09NaN1126163.060.0NaN93149858.0NaNNaN249.0NaN
2425514189847NaN1.062818125967808.020.0NaN161128.0NaN10.02020-07-16 16:04:00862108.02.001931e+09NaN1126163.060.0NaN93149858.0NaNNaN249.0NaN
2425524189849NaN1.062818307985702.020.0NaN161113.0NaN10.02020-07-16 14:38:00863007.02.001933e+09NaN461379.060.0NaN27605317.0NaNNaN248.0NaN
2425534189851NaN1.062817937986103.020.0NaN161185.0NaN10.02020-07-16 06:03:00864705.02.001934e+09NaN1126631.060.0NaN55472826.0NaNNaN250.0NaN
2425544189871NaN1.06281574176404.020.0NaN161128.0NaN10.02020-07-16 02:23:00863007.02.001933e+09NaN39513543.060.0NaN54998897.0NaNNaN249.0NaN
2425554189873NaN1.062815154986103.020.0NaN161185.0NaN10.02020-07-16 06:03:00864705.02.001934e+09NaN1126631.060.0NaN55472826.0NaNNaN245.0NaN
2425564189884NaN1.062816756967808.020.0NaN161128.0NaN10.02020-07-16 16:04:00862108.02.001931e+09NaN1126163.060.0NaN93149858.0NaNNaN250.0NaN
2425574189887NaN1.062816178986103.020.0NaN161185.0NaN10.02020-07-16 06:03:00864705.02.001934e+09NaN1126631.060.0NaN55472826.0NaNNaN238.0NaN
2425584189902NaN1.062813852592204.020.0NaN161043.0NaN10.02020-07-16 00:59:00863100.02.001931e+09NaN5757665.060.0NaN43830663.0NaNNaN247.0NaN